Endometrial Cancer Staging: Is There Value in ADC?

Moreira, Ana Sofia Linhares; Ribeiro, Vera; Aringhieri, Giacomo; Fanni, Salvatore Claudio; Tumminello, Lorenzo; Faggioni, Lorenzo; Cioni, Dania; Neri, Emanuele

doi:10.3390/jpm13050728

Cited by 3 publications

(3 citation statements)

References 34 publications

(63 reference statements)

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“…The most frequently used protocol in our study was T2-WI in three planes combined with sagittal DCE. According to the ESUR guidelines [ 12 ], the recommended scanning protocol for staging local disease in EC includes T2, DCE and DWI, but equal accuracies have been reported with protocols omitting DCE or DWI [ 2 , 7 , 8 , 11 , 12 , 13 , 16 , 17 , 26 , 34 ]. It has been advocated that DCE may be safely omitted when including DWI in patients in whom Gadolinium is contraindicated [ 18 ], which was the case in 4.8% (6/126) of the patients in our study.…”

Section: Discussionmentioning

confidence: 99%

Performance of MRI for Detection of ≥pT1b Disease in Local Staging of Endometrial Cancer

Van Vynckt,

Tummers,

Denys

et al. 2024

Cancers

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Magnetic resonance imaging (MRI) can be used for the preoperative local staging of endometrial cancer (EC). The presence of ≥pT1b disease (i.e., tumor invasion in ≥50% of the myometrium, into the cervical stroma or spread outside the uterus) has important prognostic value and implications for the decision to perform lymphadenectomy. The purpose of this study was to assess the performance of MRI for the detection of ≥pT1b disease and to evaluate whether tumor size measured via MRI was predictive for ≥pT1b disease, independent of imaging signs of deep invasion. MRI T-staging and tumor diameter and volume were correlated with histopathology of the hysterectomy specimen in 126 patients. MRI had a sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 70.0%, 83.3%, 79.2%, 75.3% and 77.0%, respectively, for the detection of ≥pT1b disease. A tumor diameter of ≥40 mm and volume of ≥20 mL measured via MRI were predictive for ≥pT1b disease at rates of 78.3% and 87.1%, respectively. An EC size of at least 5 mm upon MRI was predictive for ≥pT1b disease in more than 50% of cases. Our results support the use of MRI in the preoperative staging of EC and suggest including size criteria in EC staging guidelines.

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Section: Discussionmentioning

confidence: 99%

Performance of MRI for Detection of ≥pT1b Disease in Local Staging of Endometrial Cancer

Van Vynckt,

Tummers,

Denys

et al. 2024

Cancers

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“…Endometrial cancer (EC) is the most prevalent gynecological tumor in Europe and in the United States, with respectively 130,051 and 101,672 cases [1,2]. In Italy, it represents the third most common cancer in women aged between 50 and 69 years, accounting for approximately 8300 new cases each year [3].…”

Section: Introductionmentioning

confidence: 99%

Molecular Profiling of H-MSI/dMMR/for Endometrial Cancer Patients: “New Challenges in Diagnostic Routine Practice”

Adorisio,

Troncone,

Barberis

et al. 2024

JMP

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Endometrial cancer (EC) represents one of the most newly diagnosed cancers across gynecological malignancies. In particular, a plethora of risk factors (both biological and lifestyle-related) drastically impact the incidence rate of novel diagnosis accounting for 8300 cases/year. In the recent era of precision medicine EC molecular classification, integrating ESGO/ESTRO/ESP guidelines, four distinct diagnostic groups have been established including POLE-mutant (POLE-pos); High-instability MSI (H-MSI)–MMR-deficient (MMR-d); p53-abnormal (p53abn); and non-specific molecular profile (NSMP), also known as p53-wild-type EC patients on the basis of clinically relevant emerging biomarkers. In addition, molecular testing also plays a pivotal role in defining the best therapeutical option. In this scenario, the European Society for Medical Oncology (ESMO) recommended d-MMR/MSI-H status evaluation in the diagnostic workflow of Lynch syndrome or selecting EC patients that could benefit from immune checkpoint inhibitors (ICIs). Although immunohistochemistry (IHC) is considered the gold standard approach for d-MMR profiling, a series of molecular PCR-based techniques have rapidly developed to integrate H-MSI status in routine practice. Here, we technically overviewed the most relevant commercially available diagnostic assays for the determination of the H-MSI/dMMR status in EC patients.

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“…It is also helpful in prognosis evaluation in terms of chemotherapy response prediction in malignancies such as breast cancer, rectal cancer, osteosarcoma, and hepatic metastasis from colorectal cancer [9][10][11][12]. Concerning prognosis prediction, lower values of the ADC suggest more aggressive histologic types and grades, and worse prognosis [13][14][15], whereas there is also a report that states suspicion in the usefulness of ADC values in determining the histological grade of malignancy, despite the excellence of MRI staging in endometrial cancer [16].…”

Section: Introductionmentioning

confidence: 99%

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI

Moon,

Yang,

Kim

et al. 2023

Diagnostics

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Background: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). Methods: DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. Results: 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. Conclusions: Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.

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Endometrial Cancer Staging: Is There Value in ADC?

Cited by 3 publications

References 34 publications

Performance of MRI for Detection of ≥pT1b Disease in Local Staging of Endometrial Cancer

Performance of MRI for Detection of ≥pT1b Disease in Local Staging of Endometrial Cancer

Molecular Profiling of H-MSI/dMMR/for Endometrial Cancer Patients: “New Challenges in Diagnostic Routine Practice”

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI

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